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The thesis is concerned with an investigation of the possibilities of generating metric information and carrying out topographic mapping operations from side-looking radar images acquired from Earthorbiting satellites, as exemplified by the synthetic aperture radar (SAR) system flown onboard the Seasat satellite, Besides the theoretical analysis of the problem, several images covering test areas with different topographic characteristics have been used for extensive and comprehensive tests of the geometric accuracy of the SAR system; for experiments with digital monoplotting techniques applied to the SAR images; and for tests concerned with the detection and"interpretation of objects appearing on these images. The results show that metric information of a limited accuracy can be obtained from satellite SAR images. This could act as the basis for reconnaissance-type mapping at scales of 1: 250,000 and smaller. The geometric accuracy actually achieved does, however, depend heavily on the method used initially to process the SAR image data. In this respect, the results obtained with the digitally processed images are superior to those obtained with the optically processed images. The influence of the topographic relief present on the ground is also noticeable and various techniques have been devised and used to eliminate or substantially reduce this effect. The use of digital monoplotting techniques did not produce as good or as complete a rectification as expected due to the difficulties experienced with the interpretation of the terrain objects recorded on the SAR images. These result partly from the constraints in imaging direction that are an inherent feature of SAR imaging and which make the detection and interpretation of certain objects on an SAR image rather arbitrary. A further difficulty is the presence of background clutter on all the Seasat SAR images but which is especially noticeable on the optically processed images tested. At the present stage of the development and application of satellite SAR imagery for mapping, the limitations are centered around shortcomings in the image resolution and quality rather than the geometric characteristics of the imagery or the rectification techniques which have been devised and implemented for mapping purposes.

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Most of the operating vessel traffic management systems experience problems, such as track loss and track swap, which may cause confusion to the traffic regulators and lead to potential hazards in the harbour operation. The reason is mainly due to the limited adaptive capabilities of the algorithms used in the detection process. The decision on whether a target is present is usually based on the magnitude of the returning echoes. Such a method has a low efficiency in discriminating between the target and clutter, especially when the signal to noise ratio is low. The performance of radar target detection depends on the features, which can be used to discriminate between clutter and targets. To have a significant improvement in the detection of weak targets, more obvious discriminating features must be identified and extracted. This research investigates conventional Constant False Alarm Rate (CFAR) algorithms and introduces the approach of applying ar1ificial intelligence methods to the target detection problems. Previous research has been unde11aken to improve the detection capability of the radar system in the heavy clutter environment and many new CFAR algorithms, which are based on amplitude information only, have been developed. This research studies these algorithms and proposes that it is feasible to design and develop an advanced target detection system that is capable of discriminating targets from clutters by learning the .different features extracted from radar returns. The approach adopted for this further work into target detection was the use of neural networks. Results presented show that such a network is able to learn particular features of specific radar return signals, e.g. rain clutter, sea clutter, target, and to decide if a target is present in a finite window of data. The work includes a study of the characteristics of radar signals and identification of the features that can be used in the process of effective detection. The use of a general purpose marine radar has allowed the collection of live signals from the Plymouth harbour for analysis, training and validation. The approach of using data from the real environment has enabled the developed detection system to be exposed to real clutter conditions that cannot be obtained when using simulated data. The performance of the neural network detection system is evaluated with further recorded data and the results obtained are compared with the conventional CFAR algorithms. It is shown that the neural system can learn the features of specific radar signals and provide a superior performance in detecting targets from clutters. Areas for further research and development arc presented; these include the use of a sophisticated recording system, high speed processors and the potential for target classification.

Bibliography: p. 363-371. / xxix, 371 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This thesis provides a systematic analysis of the performance of a number of spaced antenna radar processing techniques. / Thesis (Ph.D.)--University of Adelaide, Dept. of Physics and Mathematical Physics, 1995

This thesis constitutes a general survey and a study of significant extensions to the usual conventional satellite radar altimetry. Historically radar altimeter has been configured to the measurement of mean sea level. It is well known that other statistics such as Significant Wave Height (SWH) and wind speed are in principle recoverable from the radar echo and these are currently of great interest. It has been the aim in this thesis to optimize such measurements, for a general meteorological application, with less interest shown in absolute measurement of sea level. Current technology makes possible a total Earth survey using a constellation of small satellites, covering the entire Earth sea surface with short revisit time. Such solutions necessitate less cost, lower power, and less precise attitude control than the scientific satellites used hitherto. The purpose of this thesis is to present a novel two mode radar altimeter for sea state monitoring. SWH is still measured by conventional high-resolution mode, which is not sensitive to off-nadir pointing. An additional novel low-resolution mode is proposed for wind speed measurement. By using this mode, wind speed measurement is much more robust to pointing error than by using conventional high-resolution mode. An improved wind speed measurement can be achieved by using a cost effective small satellite. Some considerable time was also spent on incorporating SAR (Synthetic Aperture Radar) into altimetry techniques to improve the signal to noise ratio. For sea state monitoring the improvements are relatively disappointing, although greater improvement are expected for ice sheet monitoring.

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The aim of this work was to investigate signal processing and analysis techniques for Ground Penetrating Radar (GPR) and its use in civil engineering and construction industry. GPR is the general term applied to techniques which employ radio waves, typically in the Mega Hertz and Giga Hertz range, to map structures and features buried in the ground or in manmade structures. GPR measurements can suffer from large amount of noise. This is primarily caused by interference from other radio-wave-emitting devices (e.g., cell phones, radios, etc.) that are present in the surrounding area of the GPR system during data collection. In addition to noise, presence of clutter – reflections from other non-target objects buried underground in the vicinity of the target can make GPR measurement difficult to understand and interpret, even for the skilled human, GPR analysts. This thesis is concerned with the improvements and processes that can be applied to GPR data in order to enhance target detection and characterisation process particularly with multivariate signal processing techniques. Those primarily include Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Both techniques have been investigated, implemented and compared regarding their abilities to separate the target originating signals from the noise and clutter type signals present in the data. Combination of PCA and ICA (SVDPICA) and two-dimensional PCA (2DPCA) are the specific approaches adopted and further developed in this work. Ability of those methods to reduce the amount of clutter and unwanted signals present in GPR data have been investigated and reported in this thesis, suggesting that their use in automated analysis of GPR images is a possibility. Further analysis carried out in this work concentrated on analysing the performance of developed multivariate signal processing techniques and at the same time investigating the possibility of identifying and characterising the features of interest in pre-processed GPR images. The driving idea behind this part of work was to extract the resonant modes present in the individual traces of each GPR image and to use properties of those poles to characterise target. Three related but different methods have been implemented and applied in this work – Extended Prony, Linear Prediction Singular Value Decomposition and Matrix Pencil methods. In addition to these approaches, PCA technique has been used to reduce dimensionality of extracted traces and to compare signals measured in various experimental setups. Performance analysis shows that Matrix Pencil offers the best results.

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